Deep Self-Reconstruction Sparse Canonical Correlation Analysis For Brain Imaging Genetics

Abstract

Brain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. As a bi-multivariate technique for brain imaging genetics, sparse canonical correlation analysis (SCCA) has the good ability to identify complex multi-SNP-multi-QT associations. However, for most current brain imaging genetics with SCCA, there exist three main challenges for calculating accurate bi-multivariate relationships and selecting relevant features, i.e., nonlinearity, high-dimensionality (across all 4005 network edges between 90 brain regions), and a small number of subjects. We propose a novel deep self-reconstruction sparse canonical correlation analysis (DS-SCCA) for solving mentioned challenges in brain imaging genetics problems. Specifically, we employ deep network, i.e., multiple stacked layers of nonlinear transformation, as the kernel function, and learn the self-reconstruction matrix to reconstruct the original data at the top layer of the network. The parameters of our model are iteratively learned using parametric approach, augmented Lagrange method, and stochastic gradient descent for optimization. Experimental results on ADNI dataset are given to demonstrate that our method produces improved cross-validation performances and biologically meaningful results.